


'''simple docstring'''






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase       							=				logging.get_logger(__name__)


def    _snake_case  (	A			)		->       str:
							lowerCAmelCase__			  =      MobileViTConfig()

							# size of the architecture
							if "mobilevit_s" in mobilevit_name:
														lowerCAmelCase__			  =      [144, 192, 240]
														lowerCAmelCase__			  =      [16, 32, 64, 96, 128, 160, 640]
							elif "mobilevit_xs" in mobilevit_name:
														lowerCAmelCase__			  =      [96, 120, 144]
														lowerCAmelCase__			  =      [16, 32, 48, 64, 80, 96, 384]
							elif "mobilevit_xxs" in mobilevit_name:
														lowerCAmelCase__			  =      [64, 80, 96]
														lowerCAmelCase__			  =      [16, 16, 24, 48, 64, 80, 320]
														lowerCAmelCase__			  =      0.05
														lowerCAmelCase__			  =      2.0

							if mobilevit_name.startswith('''deeplabv3_'''			):
														lowerCAmelCase__			  =      512
														lowerCAmelCase__			  =      16
														lowerCAmelCase__			  =      21
														lowerCAmelCase__			  =      '''pascal-voc-id2label.json'''
							else:
														lowerCAmelCase__			  =      1000
														lowerCAmelCase__			  =      '''imagenet-1k-id2label.json'''

							lowerCAmelCase__			  =      '''huggingface/label-files'''
							lowerCAmelCase__			  =      json.load(open(hf_hub_download(A					,       A					,       repo_type='''dataset'''			)					,       '''r'''			)			)
							lowerCAmelCase__			  =      {int(A			): v for k, v in idalabel.items()}
							lowerCAmelCase__			  =      idalabel
							lowerCAmelCase__			  =      {v: k for k, v in idalabel.items()}

							return config


def    _snake_case  (	A					,       A=False			)		->       str:
							for i in range(1					,       6			):
														if F"""layer_{i}.""" in name:
																					lowerCAmelCase__			  =      name.replace(F"""layer_{i}."""					,       F"""encoder.layer.{i - 1}."""			)

							if "conv_1." in name:
														lowerCAmelCase__			  =      name.replace('''conv_1.'''					,       '''conv_stem.'''			)
							if ".block." in name:
														lowerCAmelCase__			  =      name.replace('''.block.'''					,       '''.'''			)

							if "exp_1x1" in name:
														lowerCAmelCase__			  =      name.replace('''exp_1x1'''					,       '''expand_1x1'''			)
							if "red_1x1" in name:
														lowerCAmelCase__			  =      name.replace('''red_1x1'''					,       '''reduce_1x1'''			)
							if ".local_rep.conv_3x3." in name:
														lowerCAmelCase__			  =      name.replace('''.local_rep.conv_3x3.'''					,       '''.conv_kxk.'''			)
							if ".local_rep.conv_1x1." in name:
														lowerCAmelCase__			  =      name.replace('''.local_rep.conv_1x1.'''					,       '''.conv_1x1.'''			)
							if ".norm." in name:
														lowerCAmelCase__			  =      name.replace('''.norm.'''					,       '''.normalization.'''			)
							if ".conv." in name:
														lowerCAmelCase__			  =      name.replace('''.conv.'''					,       '''.convolution.'''			)
							if ".conv_proj." in name:
														lowerCAmelCase__			  =      name.replace('''.conv_proj.'''					,       '''.conv_projection.'''			)

							for i in range(0					,       2			):
														for j in range(0					,       4			):
																					if F""".{i}.{j}.""" in name:
																												lowerCAmelCase__			  =      name.replace(F""".{i}.{j}."""					,       F""".{i}.layer.{j}."""			)

							for i in range(2					,       6			):
														for j in range(0					,       4			):
																					if F""".{i}.{j}.""" in name:
																												lowerCAmelCase__			  =      name.replace(F""".{i}.{j}."""					,       F""".{i}."""			)
																												if "expand_1x1" in name:
																																			lowerCAmelCase__			  =      name.replace('''expand_1x1'''					,       '''downsampling_layer.expand_1x1'''			)
																												if "conv_3x3" in name:
																																			lowerCAmelCase__			  =      name.replace('''conv_3x3'''					,       '''downsampling_layer.conv_3x3'''			)
																												if "reduce_1x1" in name:
																																			lowerCAmelCase__			  =      name.replace('''reduce_1x1'''					,       '''downsampling_layer.reduce_1x1'''			)

							for i in range(2					,       5			):
														if F""".global_rep.{i}.weight""" in name:
																					lowerCAmelCase__			  =      name.replace(F""".global_rep.{i}.weight"""					,       '''.layernorm.weight'''			)
														if F""".global_rep.{i}.bias""" in name:
																					lowerCAmelCase__			  =      name.replace(F""".global_rep.{i}.bias"""					,       '''.layernorm.bias'''			)

							if ".global_rep." in name:
														lowerCAmelCase__			  =      name.replace('''.global_rep.'''					,       '''.transformer.'''			)
							if ".pre_norm_mha.0." in name:
														lowerCAmelCase__			  =      name.replace('''.pre_norm_mha.0.'''					,       '''.layernorm_before.'''			)
							if ".pre_norm_mha.1.out_proj." in name:
														lowerCAmelCase__			  =      name.replace('''.pre_norm_mha.1.out_proj.'''					,       '''.attention.output.dense.'''			)
							if ".pre_norm_ffn.0." in name:
														lowerCAmelCase__			  =      name.replace('''.pre_norm_ffn.0.'''					,       '''.layernorm_after.'''			)
							if ".pre_norm_ffn.1." in name:
														lowerCAmelCase__			  =      name.replace('''.pre_norm_ffn.1.'''					,       '''.intermediate.dense.'''			)
							if ".pre_norm_ffn.4." in name:
														lowerCAmelCase__			  =      name.replace('''.pre_norm_ffn.4.'''					,       '''.output.dense.'''			)
							if ".transformer." in name:
														lowerCAmelCase__			  =      name.replace('''.transformer.'''					,       '''.transformer.layer.'''			)

							if ".aspp_layer." in name:
														lowerCAmelCase__			  =      name.replace('''.aspp_layer.'''					,       '''.'''			)
							if ".aspp_pool." in name:
														lowerCAmelCase__			  =      name.replace('''.aspp_pool.'''					,       '''.'''			)
							if "seg_head." in name:
														lowerCAmelCase__			  =      name.replace('''seg_head.'''					,       '''segmentation_head.'''			)
							if "segmentation_head.classifier.classifier." in name:
														lowerCAmelCase__			  =      name.replace('''segmentation_head.classifier.classifier.'''					,       '''segmentation_head.classifier.'''			)

							if "classifier.fc." in name:
														lowerCAmelCase__			  =      name.replace('''classifier.fc.'''					,       '''classifier.'''			)
							elif (not base_model) and ("segmentation_head." not in name):
														lowerCAmelCase__			  =      '''mobilevit.''' + name

							return name


def    _snake_case  (	A					,       A					,       A=False			)		->       Tuple:
							if base_model:
														lowerCAmelCase__			  =      ''''''
							else:
														lowerCAmelCase__			  =      '''mobilevit.'''

							for key in orig_state_dict.copy().keys():
														lowerCAmelCase__			  =      orig_state_dict.pop(A			)

														if key[:8] == "encoder.":
																					lowerCAmelCase__			  =      key[8:]

														if "qkv" in key:
																					lowerCAmelCase__			  =      key.split('''.'''			)
																					lowerCAmelCase__			  =      int(key_split[0][6:]			) - 1
																					lowerCAmelCase__			  =      int(key_split[3]			)
																					lowerCAmelCase__			  =      model.get_submodule(F"""{model_prefix}encoder.layer.{layer_num}"""			)
																					lowerCAmelCase__			  =      layer.transformer.layer[transformer_num].attention.attention.all_head_size
																					lowerCAmelCase__			  =      (
																					    F"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
																					)
																					if "weight" in key:
																												lowerCAmelCase__			  =      val[:dim, :]
																												lowerCAmelCase__			  =      val[dim : dim * 2, :]
																												lowerCAmelCase__			  =      val[-dim:, :]
																					else:
																												lowerCAmelCase__			  =      val[:dim]
																												lowerCAmelCase__			  =      val[dim : dim * 2]
																												lowerCAmelCase__			  =      val[-dim:]
														else:
																					lowerCAmelCase__			  =      val

							return orig_state_dict


def    _snake_case  (	)		->       Dict:
							lowerCAmelCase__			  =      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
							lowerCAmelCase__			  =      Image.open(requests.get(A					,       stream=A			).raw			)
							return im


@torch.no_grad()
def    _snake_case  (	A					,       A					,       A					,       A=False			)		->       int:
							lowerCAmelCase__			  =      get_mobilevit_config(A			)

							# load original state_dict
							lowerCAmelCase__			  =      torch.load(A					,       map_location='''cpu'''			)

							# load 🤗 model
							if mobilevit_name.startswith('''deeplabv3_'''			):
														lowerCAmelCase__			  =      MobileViTForSemanticSegmentation(A			).eval()
							else:
														lowerCAmelCase__			  =      MobileViTForImageClassification(A			).eval()

							lowerCAmelCase__			  =      convert_state_dict(A					,       A			)
							model.load_state_dict(A			)

							# Check outputs on an image, prepared by MobileViTImageProcessor
							lowerCAmelCase__			  =      MobileViTImageProcessor(crop_size=config.image_size					,       size=config.image_size + 32			)
							lowerCAmelCase__			  =      image_processor(images=prepare_img()					,       return_tensors='''pt'''			)
							lowerCAmelCase__			  =      model(**A			)
							lowerCAmelCase__			  =      outputs.logits

							if mobilevit_name.startswith('''deeplabv3_'''			):
														assert logits.shape == (1, 21, 32, 32)

														if mobilevit_name == "deeplabv3_mobilevit_s":
																					lowerCAmelCase__			  =      torch.tensor(
																					    [
																					        [[6.2_065, 6.1_292, 6.2_070], [6.1_079, 6.1_254, 6.1_747], [6.0_042, 6.1_071, 6.1_034]],
																					        [[-6.9_253, -6.8_653, -7.0_398], [-7.3_218, -7.3_983, -7.3_670], [-7.1_961, -7.2_482, -7.1_569]],
																					        [[-4.4_723, -4.4_348, -4.3_769], [-5.3_629, -5.4_632, -5.4_598], [-5.1_587, -5.3_402, -5.5_059]],
																					    ]			)
														elif mobilevit_name == "deeplabv3_mobilevit_xs":
																					lowerCAmelCase__			  =      torch.tensor(
																					    [
																					        [[5.4_449, 5.5_733, 5.6_314], [5.1_815, 5.3_930, 5.5_963], [5.1_656, 5.4_333, 5.4_853]],
																					        [[-9.4_423, -9.7_766, -9.6_714], [-9.1_581, -9.5_720, -9.5_519], [-9.1_006, -9.6_458, -9.5_703]],
																					        [[-7.7_721, -7.3_716, -7.1_583], [-8.4_599, -8.0_624, -7.7_944], [-8.4_172, -7.8_366, -7.5_025]],
																					    ]			)
														elif mobilevit_name == "deeplabv3_mobilevit_xxs":
																					lowerCAmelCase__			  =      torch.tensor(
																					    [
																					        [[6.9_811, 6.9_743, 7.3_123], [7.1_777, 7.1_931, 7.3_938], [7.5_633, 7.8_050, 7.8_901]],
																					        [[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_624, -9.5_964], [-10.8_840, -10.8_158, -10.6_659]],
																					        [[-3.4_938, -3.0_631, -2.8_620], [-3.4_205, -2.8_135, -2.6_875], [-3.4_179, -2.7_945, -2.8_750]],
																					    ]			)
														else:
																					raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""			)

														assert torch.allclose(logits[0, :3, :3, :3]					,       A					,       atol=1E-4			)
							else:
														assert logits.shape == (1, 1000)

														if mobilevit_name == "mobilevit_s":
																					lowerCAmelCase__			  =      torch.tensor([-0.9_866, 0.2_392, -1.1_241]			)
														elif mobilevit_name == "mobilevit_xs":
																					lowerCAmelCase__			  =      torch.tensor([-2.4_761, -0.9_399, -1.9_587]			)
														elif mobilevit_name == "mobilevit_xxs":
																					lowerCAmelCase__			  =      torch.tensor([-1.9_364, -1.2_327, -0.4_653]			)
														else:
																					raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""			)

														assert torch.allclose(logits[0, :3]					,       A					,       atol=1E-4			)

							Path(A			).mkdir(exist_ok=A			)
							print(F"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}"""			)
							model.save_pretrained(A			)
							print(F"""Saving image processor to {pytorch_dump_folder_path}"""			)
							image_processor.save_pretrained(A			)

							if push_to_hub:
														lowerCAmelCase__			  =      {
														    '''mobilevit_s''': '''mobilevit-small''',
														    '''mobilevit_xs''': '''mobilevit-x-small''',
														    '''mobilevit_xxs''': '''mobilevit-xx-small''',
														    '''deeplabv3_mobilevit_s''': '''deeplabv3-mobilevit-small''',
														    '''deeplabv3_mobilevit_xs''': '''deeplabv3-mobilevit-x-small''',
														    '''deeplabv3_mobilevit_xxs''': '''deeplabv3-mobilevit-xx-small''',
														}

														print('''Pushing to the hub...'''			)
														lowerCAmelCase__			  =      model_mapping[mobilevit_name]
														image_processor.push_to_hub(A					,       organization='''apple'''			)
														model.push_to_hub(A					,       organization='''apple'''			)


if __name__ == "__main__":
		__UpperCAmelCase       							=				argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--mobilevit_name''',
		    default='''mobilevit_s''',
		    type=str,
		    help=(
		        '''Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','''
		        ''' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'''
		    ),
		)
		parser.add_argument(
		    '''--checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
		)
		parser.add_argument(
		    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
		)

		__UpperCAmelCase       							=				parser.parse_args()
		convert_movilevit_checkpoint(
		    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)





'''simple docstring'''







import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=True      ,UpperCAmelCase="pt"						):






   '''simple docstring'''







   A__       =     {'add_prefix_space': True} if isinstance(UpperCAmelCase      ,UpperCAmelCase						) and not line.startswith(' '						) else {}
   A__       =     padding_side
   return tokenizer(
       [line]      ,max_length=UpperCAmelCase      ,padding='max_length' if pad_to_max_length else None      ,truncation=UpperCAmelCase      ,return_tensors=UpperCAmelCase      ,add_special_tokens=UpperCAmelCase      ,**UpperCAmelCase      ,)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=None      ,):






   '''simple docstring'''







   A__       =     input_ids.ne(UpperCAmelCase						).any(dim=0						)
   if attention_mask is None:
      return input_ids[:, keep_column_mask]
   else:
      return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])

class 	_snake_case(			UpperCAmelCase      ):






 def __init__(self       :  List[str]    ,		a       :  Any    ,		a       :  Union[str, Any]    ,		a       :  int    ,		a       :  Optional[int]    ,		a       :  Optional[Any]="train"    ,		a       :  Optional[int]=None    ,		a       :  Tuple=None    ,		a       :  str=None    ,		a       :  Dict=""    ,		)       ->   Dict:






    """simple docstring"""


    super().__init__()
    A__       =     Path(a    ).joinpath(type_path + '.source'    )
    A__       =     Path(a    ).joinpath(type_path + '.target'    )
    A__       =     self.get_char_lens(self.src_file    )
    A__       =     max_source_length
    A__       =     max_target_length
    assert min(self.src_lens    ) > 0, f"""found empty line in {self.src_file}"""
    A__       =     tokenizer
    A__       =     prefix
    if n_obs is not None:
       A__       =     self.src_lens[:n_obs]
    A__       =     src_lang
    A__       =     tgt_lang






 def __len__(self       :  Optional[Any]    )       ->   List[str]:






    """simple docstring"""


    return len(self.src_lens    )






 def __getitem__(self       :  Any    ,		a       :  str    )       ->   Dict[str, torch.Tensor]:






    """simple docstring"""


    A__       =     index + 1  # linecache starts at 1
    A__       =     self.prefix + linecache.getline(str(self.src_file    )    ,		a    ).rstrip('\n'    )
    A__       =     linecache.getline(str(self.tgt_file    )    ,		a    ).rstrip('\n'    )
    assert source_line, f"""empty source line for index {index}"""
    assert tgt_line, f"""empty tgt line for index {index}"""

    # Need to add eos token manually for T5
    if isinstance(self.tokenizer    ,		a    ):
       source_line += self.tokenizer.eos_token
       tgt_line += self.tokenizer.eos_token

    # Pad source and target to the right
    A__       =     (
        self.tokenizer.question_encoder if isinstance(self.tokenizer    ,		a    ) else self.tokenizer
    )
    A__       =     self.tokenizer.generator if isinstance(self.tokenizer    ,		a    ) else self.tokenizer

    A__       =     encode_line(a    ,		a    ,		self.max_source_length    ,		'right'    )
    A__       =     encode_line(a    ,		a    ,		self.max_target_length    ,		'right'    )

    A__       =     source_inputs['input_ids'].squeeze()
    A__       =     target_inputs['input_ids'].squeeze()
    A__       =     source_inputs['attention_mask'].squeeze()
    return {
        "input_ids": source_ids,
        "attention_mask": src_mask,
        "decoder_input_ids": target_ids,
    }






 @staticmethod
 def 						_UpperCamelCase (a       :  Tuple    )       ->   Tuple:






    """simple docstring"""


    return [len(a    ) for x in Path(a    ).open().readlines()]






 def 						_UpperCamelCase (self       :  str    ,		a       :  int    )       ->   Dict[str, torch.Tensor]:






    """simple docstring"""


    A__       =     torch.stack([x['input_ids'] for x in batch]    )
    A__       =     torch.stack([x['attention_mask'] for x in batch]    )
    A__       =     torch.stack([x['decoder_input_ids'] for x in batch]    )
    A__       =     (
        self.tokenizer.generator.pad_token_id
        if isinstance(self.tokenizer    ,		a    )
        else self.tokenizer.pad_token_id
    )
    A__       =     (
        self.tokenizer.question_encoder.pad_token_id
        if isinstance(self.tokenizer    ,		a    )
        else self.tokenizer.pad_token_id
    )
    A__       =     trim_batch(a    ,		a    )
    A__  ,			A__       =     trim_batch(a    ,		a    ,		attention_mask=a    )
    A__       =     {
        'input_ids': source_ids,
        'attention_mask': source_mask,
        'decoder_input_ids': y,
    }
    return batch




lowerCAmelCase_     						=				getLogger(__name__)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   return list(itertools.chain.from_iterable(UpperCAmelCase						)						)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   A__       =     get_git_info()
   save_json(UpperCAmelCase      ,os.path.join(UpperCAmelCase      ,'git_log.json'						)						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase=4      ,**UpperCAmelCase						):






   '''simple docstring'''







   with open(UpperCAmelCase      ,'w'						) as f:
      json.dump(UpperCAmelCase      ,UpperCAmelCase      ,indent=UpperCAmelCase      ,**UpperCAmelCase						)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   with open(UpperCAmelCase						) as f:
      return json.load(UpperCAmelCase						)







def 						_A      ( ):






   '''simple docstring'''







   A__       =     git.Repo(search_parent_directories=UpperCAmelCase						)
   A__       =     {
       'repo_id': str(UpperCAmelCase						),
       'repo_sha': str(repo.head.object.hexsha						),
       'repo_branch': str(repo.active_branch						),
       'hostname': str(socket.gethostname()						),
   }
   return repo_infos







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   return list(map(UpperCAmelCase      ,UpperCAmelCase						)						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   with open(UpperCAmelCase      ,'wb'						) as f:
      return pickle.dump(UpperCAmelCase      ,UpperCAmelCase						)







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   def remove_articles(UpperCAmelCase						):
      return re.sub(r'\b(a|an|the)\b'      ,' '      ,UpperCAmelCase						)

   def white_space_fix(UpperCAmelCase						):
      return " ".join(text.split()						)

   def remove_punc(UpperCAmelCase						):
      A__       =     set(string.punctuation						)
      return "".join(ch for ch in text if ch not in exclude						)

   def lower(UpperCAmelCase						):
      return text.lower()

   return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase						)						)						)						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     normalize_answer(UpperCAmelCase						).split()
   A__       =     normalize_answer(UpperCAmelCase						).split()
   A__       =     Counter(UpperCAmelCase						) & Counter(UpperCAmelCase						)
   A__       =     sum(common.values()						)
   if num_same == 0:
      return 0
   A__       =     1.0 * num_same / len(UpperCAmelCase						)
   A__       =     1.0 * num_same / len(UpperCAmelCase						)
   A__       =     (2 * precision * recall) / (precision + recall)
   return fa







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   return normalize_answer(UpperCAmelCase						) == normalize_answer(UpperCAmelCase						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   assert len(UpperCAmelCase						) == len(UpperCAmelCase						)
   A__       =     0
   for hypo, pred in zip(UpperCAmelCase      ,UpperCAmelCase						):
      em += exact_match_score(UpperCAmelCase      ,UpperCAmelCase						)
   if len(UpperCAmelCase						) > 0:
      em /= len(UpperCAmelCase						)
   return {"em": em}







def 						_A      ( UpperCAmelCase						):






   '''simple docstring'''







   return model_prefix.startswith('rag'						)







def 						_A      ( UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):






   '''simple docstring'''







   A__       =     {p: p for p in extra_params}
   # T5 models don't have `dropout` param, they have `dropout_rate` instead
   A__       =     'dropout_rate'
   for p in extra_params:
      if getattr(UpperCAmelCase      ,UpperCAmelCase      ,UpperCAmelCase						):
         if not hasattr(UpperCAmelCase      ,UpperCAmelCase						) and not hasattr(UpperCAmelCase      ,equivalent_param[p]						):
            logger.info('config doesn\'t have a `{}` attribute'.format(UpperCAmelCase						)						)
            delattr(UpperCAmelCase      ,UpperCAmelCase						)
            continue
         A__       =     p if hasattr(UpperCAmelCase      ,UpperCAmelCase						) else equivalent_param[p]
         setattr(UpperCAmelCase      ,UpperCAmelCase      ,getattr(UpperCAmelCase      ,UpperCAmelCase						)						)
         delattr(UpperCAmelCase      ,UpperCAmelCase						)
   return hparams, config


